Econometrics I (fall 2017)
This is the support page for Econometrics I (Applied Econometrics with R, fall 2017). You can find materials (such as slides and sample programs) and other information for the course.
As a preparation for this course, you need to install R and related programs in your own computer. Here is a brief guide of how to install them. It is expected that you have done the installation before the second lecture.
Course Information
Time and classroom: Mon 19:00  21:20, 1700
Instructor: JiaPing HUANG
Email: huangjp #at# szu . edu . cn
Office hours: Mon/Tue 13:00  14:00
Grading: attitude (10%) + assignments (2 × 10%) + presentation (20%) + final report (50%)
Schedule and Materials

Lecture 1 (Sep 18, Week 3):
 Introduction
[Slides]
Link: [A brief installation guide for R]
 Introduction

Lecture 2 (Sep 25, Week 4):
 R installation
 R basics (1): calculation, vector, matrix
[Slides]

Lecture 3 (Oct 9, Week 6):
 R basics (2): matrix, script file and project, data frame, graphics
[Slides]
 R basics (2): matrix, script file and project, data frame, graphics

Lecture 4 (Oct 16, Week 7):
 R programing : condition, loop, recursion
 R programing practice
[Slides]

Lecture 5 (Oct 23, Week 8):
 Review of probability
[Slides]
 Review of probability

Lecture 6 (Oct 30, Week 9):
 Review of statistics (1)
[Slides]
 Review of statistics (1)

Lecture 7 (Nov 6, Week 10):
 Review of statistics (2)
 Linear regression (1): model fitting
[Slides]
[The California test score data set 19981999 (.xlsx)]
[The California test score data set 19981999 (.csv)]
[Description of the California test score data set 19981999 (.docx)]

Lecture 8 (Nov 13, Week 11):
 Linear regression (2): multivariate linear regression, hypothesis testing
[Slides]
 Linear regression (2): multivariate linear regression, hypothesis testing

Lecture 9 (Nov 20, Week 12):
 Nonlinear regression
[Slides]
 Nonlinear regression

Lecture 10 (Nov 27, Week 13):
 Binary dependent variables
[Slides]
 Binary dependent variables

Lecture 11 (Dec 4, Week 14):
 Instrumental variables
[Slides]
 Instrumental variables

Lecture 12 (Dec 11, Week 15):
 Special topic: U shape test
[Slides]
 Special topic: U shape test

Lecture 13 (Dec 18, Week 16):
 Presentation (1)

Lecture 14 (Dec 25, Week 17):
 Presentation (2)

Final report (Jan 8, 2018, Week 19)
[The US Current Population Survey data (cps08.xlsx)]
[The US Current Population Survey data description]
[The College Distance West data (CDWest.xls)]
[The College Distance West data description]
[The cover page]Hand in your report to WKL 1720 on 14:00 – 17:00 of Jan 8, 2018.
Assignments

Assignment 1 (Week 7) [Slides]
Question:
The Fibonacci sequence (or Fibonacci numbers) is \[ 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 87, 144, \cdots , \] or mathematically \[ F_n = F_{n1} + F_{n2} \quad \text{with} \quad F_2 = F_1 = 1 .\]Write two functions
fib1
andfib2
that return the Fibonacci number \(F_n\) for input \(n\). Use recursion infib1
and do NOT use recursion infib2
. Print both functions in an A4 paper. Use a fixedwidth font and take care of the readability of your codes (indentation, comments, etc.).Due date: hand in at the beginning of the class on Oct 23, 2017.
Hint: You can find explicit formulas of the Fibonacci numbers from https://en.wikipedia.org/wiki/Fibonacci_number
For example, \[ F_n = \sum_{k=0}^{\lfloor \frac{n1}{2} \rfloor} \binom{nk1}{k} ,\] where the floor function \(\lfloor \ \rfloor\) can be calculated withfloor()
, and the binomial coefficient can be calculated withchoose( , )
. 
Assignment 2 (Week 11) [Slides]
 Use the California Test Score data set (caschool.csv) to perform multiple linear regression analysis to explain test scores (
testscr
).  Take studentteach ratio (
str
) as the base specification.  Three additional variables:
 percentage of students who are still learning English
(
el_pct
),  percentage of students who are eligible for receiving a reduced priced lunch at school (
meal_pct
),  percentage of students in the district whose families qualifies for a California income assistance program (
calw_pct
).
 percentage of students who are still learning English
(
Question:
Perform multivariate linear regression analysis for the base specification and at least two alternative specifications whose regressors are chosen from str plus the three additional variables. Write down your regression models (three in total) and corresponding OLS regression results (with standard errors) in equation.
 Make a table to summarize your regression results.
 Discuss your results shortly.
Due date: hand in at the beginning of the class on Nov 20, 2017.
 Use the California Test Score data set (caschool.csv) to perform multiple linear regression analysis to explain test scores (
Reading and Presentation
 Six groups (2 person × 5 group + 3 person × 1 group)
 Each group select an applied econometrics article that is published later than 2000 in a journal from the journal list below, read it intensively, and present the contents in group.
 Each team has 30 minutes for presentation, and 10 minutes for Q&A. Each one need to speak no less than 10 minutes.
 Report the article you select on Nov 20. Presentation takes place on Dec 18 and 25.
Reading groups (in initials)

Group 1: LGZ and ZH
Zheng et al. (2017). U.S. Demand for tobacco products in a system framework. Health Economics, 26:10671086. 
Group 2: WX and DYF
Brueckner & Schwandt (2014). Income and population growth. Economic Journal, 125:16531676. 
Group 3: SHL and XY
Araujo et al. (2016). Teacher quality and learning outcomes in kindergarten. Quarterly Journal of Economics, 131:14151453. 
Group 4: XYQ and GYL
Sachs & Warner (2001). The curse of natural resources. European Economic Review, 45:827838. 
Group 5: LYJ and WY
Benjamin et al. (2012). What do you think would make you happier? What do you think you would choose? American Economic Review, 102(5):20832110. 
Group 6: ZCX, HCC, and TM
Miller et al. (2006). The return to schooling: Estimates from a sample of young Australian twins. Labour Economics, 13:571587.
Journal list (not a ranking)
 American Economic Review
 Econometrica
 Quarterly Journal of Economics
 Journal of Political Economy
 Review of Economic Studies
 Review of Economics and Statistics
 Economic Journal
 American Economic Journal: Applied Economics
 RAND Journal of Economics
 Journal of Applied Econometrics
 Journal of Business and Economic Statistics
 International Economic Review
 Journal of Econometrics
 Scandinavian Journal of Economics
 Journal of Labor Economics
 Labor Economics
 Journal of Public Economics
 Journal of Economic Growth
 Journal of Health Economics
 Health Economics
 Journal of the European Economic Association
 European Economic Review
 Oxford Bulletin of Economics and Statistics
Useful References
 Stock, J. H. and Watson, M. M., Introduction to Econometrics, 3rd Edition, Global Edition, Pearson, 2012.
（《计量经济学导论》第三版国际版，译者：张涛、巩书欣，中国人民大学出版社、2014）  Studenmund, A. H., Using Econometrics: A Practical Guide, 6th Edition, Pearson, 2011.
 Kleiber, C. and Zeileis, A., Applied Econometrics with R, Springer, 2008.
 Heiss, F., Using R for Introductory Econometrics, 2016.
 Dennis, B., 《R语言初学指南》，译者：高敬雅、刘波，人民邮电出版社，2016.
 Kabacoff, R. I., 《R语言实战》第二版，译者：王小宁等，人民邮电出版社，2016.
 QuickR, http://www.statmethods.net/
Further Readings
 Angrist, J. D. and Pischke, J.S., Mastering ‘Metrics: The Path from Cause to Effect, Princeton University Press, 2015.